Content & Deliverables
Projects
Tree Segmentation.
Project: LiDAR-Based Individual Tree Segmentation
In this project, I used LiDAR data to segment individual trees from a random plot within a forest. The goal was to apply point cloud and Canopy Height Model (CHM)-based methods for tree detection and segmentation. The process involved extracting a plot, filtering outliers, normalizing the data, and performing tree segmentation using algorithms like li2012 and dalponte2016.
Skills & Tools Used:
R Programming: Utilized R for data manipulation, analysis, and visualization.
LiDAR Processing: Used the
lidRpackage to load, filter, and normalize LiDAR data, removing outliers and correcting for ground elevation.Tree Segmentation: Applied the
li2012algorithm on the point cloud data for tree segmentation and used thedalponte2016algorithm on CHM data to detect tree tops and segment individual trees.Visualization: Created 3D visualizations of segmented trees using the
rglpackage and compared results from both methods.Raster Analysis: Generated Canopy Height Models (CHM) at 0.5m resolution using
terrato improve segmentation accuracy.
Results:
The segmentation results showed that both methods identified individual trees with high accuracy. The li2012 algorithm effectively segmented trees based on the point cloud, while the dalponte2016 method produced accurate tree crown delineation using the CHM. The visualizations confirmed that tree segmentation was successful, providing clear distinctions between individual trees.